資料分析 從R 開始
David Chiu (largitdata.com)
david@largitdata.com
2014/10/03
關於我
大數軟體(largitdata.com)有限公司創辦人
前趨勢科技工程師
ywchiu.com
什麼是R
- AT&T貝爾實驗室暨S語言所發展出來的GNU 專案
- 提供統計分析與圖形視覺化功能的開源程式語言
- 使用C, Fortran 編程的函式語言
為什麼使用R
立即完成統計分析
內建許多數學函式及圖形套件(也可安裝第三方套件)
免費且開源
R語言是
R 是函式語言 (Functional Programming)
- 給予數學定義，便可以數學函式求得解答
R 是直譯式語言 (Interpreted Language)
- 一行行執行，可直接看到執行結果
R 是物件導向語言 (Object Oriented Language)
- R可以用S3, S4定義物件
最廣泛被用來做資料分析的語言
最受歡迎的語言持續為 R, Python (39%), 及 SQL (37%). SAS 大約在 20%上下.
By Gregory Piatetsky, Aug 27, 2013.
Kaggle
R 是最廣為Kaggle比賽參與者所使用的語言
Google 的資料科學家使用 R
What is your programming language of choice, R, Python or something else?
“I use R, and occasionally matlab, for data analysis. There is a large, active and extremely knowledgeable R community at Google.”
蘋果的資料科學家也使用 R
Expert knowledge of SAS (With Enterprise Guide/Miner) required and candidates with strong knowledge of R will be preferred
商業版本的 R
在 2007, Revolution Analytics 提供商業版本的R
Big Data Appliance, 整合R, Apache Hadoop, Oracle Enterprise Linux, 和 NoSQL 資料庫於 Exadata
Distributed R
Revolution R
整合式開發環境 IDE
Rstudio
網頁應用程式開發
Shiny 讓使用者可以透過互動式網頁介面，在上面直接進行分析
RPubs
- https://rpubs.com/
- 讓使用者可以發布執行結果與原始碼到網頁上
套件管理
CRAN (Comprehensive R Archive Network)
資料分析
資料分析如同做菜
R 語言基礎
- 資料型態 (Vector,List,Factor,Array,Matrix,Data Frame)
- 自訂函式
- 控制流程
- 資料IO
- 物件導向
內建資料集
使用內建資料
data(iris)
str(iris)
'data.frame': 150 obs. of 5 variables:
$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
資料篩選 (一)
取前五筆包含length 及 width 的資料
Five.Sepal.iris = iris[1:5, c("Sepal.Length", "Sepal.Width")]
Five.Sepal.iris
Sepal.Length Sepal.Width
1 5.1 3.5
2 4.9 3.0
3 4.7 3.2
4 4.6 3.1
5 5.0 3.6
資料篩選 (二)
可以用條件做篩選
setosa.data = iris[iris$Species=="setosa",1:5]
setosa.data
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
14 4.3 3.0 1.1 0.1 setosa
15 5.8 4.0 1.2 0.2 setosa
16 5.7 4.4 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.5 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.6 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 5.0 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 5.0 3.2 1.2 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
39 4.4 3.0 1.3 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 3.5 1.6 0.6 setosa
45 5.1 3.8 1.9 0.4 setosa
46 4.8 3.0 1.4 0.3 setosa
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
資料篩選 (三)
使用which 做資料篩選
which(iris$Species=="setosa")
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
[24] 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
[47] 47 48 49 50
使用subset 做資料篩選 (一)
使用subset 取欄位
sepal.data = subset(iris, select=c("Sepal.Length", "Sepal.Width"))
sepal.data
Sepal.Length Sepal.Width
1 5.1 3.5
2 4.9 3.0
3 4.7 3.2
4 4.6 3.1
5 5.0 3.6
6 5.4 3.9
7 4.6 3.4
8 5.0 3.4
9 4.4 2.9
10 4.9 3.1
11 5.4 3.7
12 4.8 3.4
13 4.8 3.0
14 4.3 3.0
15 5.8 4.0
16 5.7 4.4
17 5.4 3.9
18 5.1 3.5
19 5.7 3.8
20 5.1 3.8
21 5.4 3.4
22 5.1 3.7
23 4.6 3.6
24 5.1 3.3
25 4.8 3.4
26 5.0 3.0
27 5.0 3.4
28 5.2 3.5
29 5.2 3.4
30 4.7 3.2
31 4.8 3.1
32 5.4 3.4
33 5.2 4.1
34 5.5 4.2
35 4.9 3.1
36 5.0 3.2
37 5.5 3.5
38 4.9 3.6
39 4.4 3.0
40 5.1 3.4
41 5.0 3.5
42 4.5 2.3
43 4.4 3.2
44 5.0 3.5
45 5.1 3.8
46 4.8 3.0
47 5.1 3.8
48 4.6 3.2
49 5.3 3.7
50 5.0 3.3
51 7.0 3.2
52 6.4 3.2
53 6.9 3.1
54 5.5 2.3
55 6.5 2.8
56 5.7 2.8
57 6.3 3.3
58 4.9 2.4
59 6.6 2.9
60 5.2 2.7
61 5.0 2.0
62 5.9 3.0
63 6.0 2.2
64 6.1 2.9
65 5.6 2.9
66 6.7 3.1
67 5.6 3.0
68 5.8 2.7
69 6.2 2.2
70 5.6 2.5
71 5.9 3.2
72 6.1 2.8
73 6.3 2.5
74 6.1 2.8
75 6.4 2.9
76 6.6 3.0
77 6.8 2.8
78 6.7 3.0
79 6.0 2.9
80 5.7 2.6
81 5.5 2.4
82 5.5 2.4
83 5.8 2.7
84 6.0 2.7
85 5.4 3.0
86 6.0 3.4
87 6.7 3.1
88 6.3 2.3
89 5.6 3.0
90 5.5 2.5
91 5.5 2.6
92 6.1 3.0
93 5.8 2.6
94 5.0 2.3
95 5.6 2.7
96 5.7 3.0
97 5.7 2.9
98 6.2 2.9
99 5.1 2.5
100 5.7 2.8
101 6.3 3.3
102 5.8 2.7
103 7.1 3.0
104 6.3 2.9
105 6.5 3.0
106 7.6 3.0
107 4.9 2.5
108 7.3 2.9
109 6.7 2.5
110 7.2 3.6
111 6.5 3.2
112 6.4 2.7
113 6.8 3.0
114 5.7 2.5
115 5.8 2.8
116 6.4 3.2
117 6.5 3.0
118 7.7 3.8
119 7.7 2.6
120 6.0 2.2
121 6.9 3.2
122 5.6 2.8
123 7.7 2.8
124 6.3 2.7
125 6.7 3.3
126 7.2 3.2
127 6.2 2.8
128 6.1 3.0
129 6.4 2.8
130 7.2 3.0
131 7.4 2.8
132 7.9 3.8
133 6.4 2.8
134 6.3 2.8
135 6.1 2.6
136 7.7 3.0
137 6.3 3.4
138 6.4 3.1
139 6.0 3.0
140 6.9 3.1
141 6.7 3.1
142 6.9 3.1
143 5.8 2.7
144 6.8 3.2
145 6.7 3.3
146 6.7 3.0
147 6.3 2.5
148 6.5 3.0
149 6.2 3.4
150 5.9 3.0
使用subset 做資料篩選 (二)
以Species 做篩選條件
setosa.data = subset(iris, Species =="setosa")
setosa.data
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
14 4.3 3.0 1.1 0.1 setosa
15 5.8 4.0 1.2 0.2 setosa
16 5.7 4.4 1.5 0.4 setosa
17 5.4 3.9 1.3 0.4 setosa
18 5.1 3.5 1.4 0.3 setosa
19 5.7 3.8 1.7 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
21 5.4 3.4 1.7 0.2 setosa
22 5.1 3.7 1.5 0.4 setosa
23 4.6 3.6 1.0 0.2 setosa
24 5.1 3.3 1.7 0.5 setosa
25 4.8 3.4 1.9 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 5.0 3.4 1.6 0.4 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
33 5.2 4.1 1.5 0.1 setosa
34 5.5 4.2 1.4 0.2 setosa
35 4.9 3.1 1.5 0.2 setosa
36 5.0 3.2 1.2 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
39 4.4 3.0 1.3 0.2 setosa
40 5.1 3.4 1.5 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
42 4.5 2.3 1.3 0.3 setosa
43 4.4 3.2 1.3 0.2 setosa
44 5.0 3.5 1.6 0.6 setosa
45 5.1 3.8 1.9 0.4 setosa
46 4.8 3.0 1.4 0.3 setosa
47 5.1 3.8 1.6 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
49 5.3 3.7 1.5 0.2 setosa
50 5.0 3.3 1.4 0.2 setosa
使用subset 做資料篩選 (三)
可以做篩選條件的組合
example.data= subset(iris, Petal.Length <=1.4 & Petal.Width >= 0.2, select=Species )
example.data
Species
1 setosa
2 setosa
3 setosa
5 setosa
7 setosa
9 setosa
15 setosa
17 setosa
18 setosa
23 setosa
29 setosa
34 setosa
36 setosa
37 setosa
39 setosa
41 setosa
42 setosa
43 setosa
46 setosa
48 setosa
50 setosa
資料合併
以Merge 做資料合併
flower.type = data.frame(Species = "setosa", Flower = "iris")
merge(flower.type, iris[1:3,], by ="Species")
Species Flower Sepal.Length Sepal.Width Petal.Length Petal.Width
1 setosa iris 5.1 3.5 1.4 0.2
2 setosa iris 4.9 3.0 1.4 0.2
3 setosa iris 4.7 3.2 1.3 0.2
資料排序
用order做資料排序
iris[order(iris$Sepal.Length, decreasing = TRUE),]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
132 7.9 3.8 6.4 2.0 virginica
118 7.7 3.8 6.7 2.2 virginica
119 7.7 2.6 6.9 2.3 virginica
123 7.7 2.8 6.7 2.0 virginica
136 7.7 3.0 6.1 2.3 virginica
106 7.6 3.0 6.6 2.1 virginica
131 7.4 2.8 6.1 1.9 virginica
108 7.3 2.9 6.3 1.8 virginica
110 7.2 3.6 6.1 2.5 virginica
126 7.2 3.2 6.0 1.8 virginica
130 7.2 3.0 5.8 1.6 virginica
103 7.1 3.0 5.9 2.1 virginica
51 7.0 3.2 4.7 1.4 versicolor
53 6.9 3.1 4.9 1.5 versicolor
121 6.9 3.2 5.7 2.3 virginica
140 6.9 3.1 5.4 2.1 virginica
142 6.9 3.1 5.1 2.3 virginica
77 6.8 2.8 4.8 1.4 versicolor
113 6.8 3.0 5.5 2.1 virginica
144 6.8 3.2 5.9 2.3 virginica
66 6.7 3.1 4.4 1.4 versicolor
78 6.7 3.0 5.0 1.7 versicolor
87 6.7 3.1 4.7 1.5 versicolor
109 6.7 2.5 5.8 1.8 virginica
125 6.7 3.3 5.7 2.1 virginica
141 6.7 3.1 5.6 2.4 virginica
145 6.7 3.3 5.7 2.5 virginica
146 6.7 3.0 5.2 2.3 virginica
59 6.6 2.9 4.6 1.3 versicolor
76 6.6 3.0 4.4 1.4 versicolor
55 6.5 2.8 4.6 1.5 versicolor
105 6.5 3.0 5.8 2.2 virginica
111 6.5 3.2 5.1 2.0 virginica
117 6.5 3.0 5.5 1.8 virginica
148 6.5 3.0 5.2 2.0 virginica
52 6.4 3.2 4.5 1.5 versicolor
75 6.4 2.9 4.3 1.3 versicolor
112 6.4 2.7 5.3 1.9 virginica
116 6.4 3.2 5.3 2.3 virginica
129 6.4 2.8 5.6 2.1 virginica
133 6.4 2.8 5.6 2.2 virginica
138 6.4 3.1 5.5 1.8 virginica
57 6.3 3.3 4.7 1.6 versicolor
73 6.3 2.5 4.9 1.5 versicolor
88 6.3 2.3 4.4 1.3 versicolor
101 6.3 3.3 6.0 2.5 virginica
104 6.3 2.9 5.6 1.8 virginica
124 6.3 2.7 4.9 1.8 virginica
134 6.3 2.8 5.1 1.5 virginica
137 6.3 3.4 5.6 2.4 virginica
147 6.3 2.5 5.0 1.9 virginica
69 6.2 2.2 4.5 1.5 versicolor
98 6.2 2.9 4.3 1.3 versicolor
127 6.2 2.8 4.8 1.8 virginica
149 6.2 3.4 5.4 2.3 virginica
64 6.1 2.9 4.7 1.4 versicolor
72 6.1 2.8 4.0 1.3 versicolor
74 6.1 2.8 4.7 1.2 versicolor
92 6.1 3.0 4.6 1.4 versicolor
128 6.1 3.0 4.9 1.8 virginica
135 6.1 2.6 5.6 1.4 virginica
63 6.0 2.2 4.0 1.0 versicolor
79 6.0 2.9 4.5 1.5 versicolor
84 6.0 2.7 5.1 1.6 versicolor
86 6.0 3.4 4.5 1.6 versicolor
120 6.0 2.2 5.0 1.5 virginica
139 6.0 3.0 4.8 1.8 virginica
62 5.9 3.0 4.2 1.5 versicolor
71 5.9 3.2 4.8 1.8 versicolor
150 5.9 3.0 5.1 1.8 virginica
15 5.8 4.0 1.2 0.2 setosa
68 5.8 2.7 4.1 1.0 versicolor
83 5.8 2.7 3.9 1.2 versicolor
93 5.8 2.6 4.0 1.2 versicolor
102 5.8 2.7 5.1 1.9 virginica
115 5.8 2.8 5.1 2.4 virginica
143 5.8 2.7 5.1 1.9 virginica
16 5.7 4.4 1.5 0.4 setosa
19 5.7 3.8 1.7 0.3 setosa
56 5.7 2.8 4.5 1.3 versicolor
80 5.7 2.6 3.5 1.0 versicolor
96 5.7 3.0 4.2 1.2 versicolor
97 5.7 2.9 4.2 1.3 versicolor
100 5.7 2.8 4.1 1.3 versicolor
114 5.7 2.5 5.0 2.0 virginica
65 5.6 2.9 3.6 1.3 versicolor
67 5.6 3.0 4.5 1.5 versicolor
70 5.6 2.5 3.9 1.1 versicolor
89 5.6 3.0 4.1 1.3 versicolor
95 5.6 2.7 4.2 1.3 versicolor
122 5.6 2.8 4.9 2.0 virginica
34 5.5 4.2 1.4 0.2 setosa
37 5.5 3.5 1.3 0.2 setosa
54 5.5 2.3 4.0 1.3 versicolor
81 5.5 2.4 3.8 1.1 versicolor
82 5.5 2.4 3.7 1.0 versicolor
90 5.5 2.5 4.0 1.3 versicolor
91 5.5 2.6 4.4 1.2 versicolor
6 5.4 3.9 1.7 0.4 setosa
11 5.4 3.7 1.5 0.2 setosa
17 5.4 3.9 1.3 0.4 setosa
21 5.4 3.4 1.7 0.2 setosa
32 5.4 3.4 1.5 0.4 setosa
85 5.4 3.0 4.5 1.5 versicolor
49 5.3 3.7 1.5 0.2 setosa
28 5.2 3.5 1.5 0.2 setosa
29 5.2 3.4 1.4 0.2 setosa
33 5.2 4.1 1.5 0.1 setosa
60 5.2 2.7 3.9 1.4 versicolor
1 5.1 3.5 1.4 0.2 setosa
18 5.1 3.5 1.4 0.3 setosa
20 5.1 3.8 1.5 0.3 setosa
22 5.1 3.7 1.5 0.4 setosa
24 5.1 3.3 1.7 0.5 setosa
40 5.1 3.4 1.5 0.2 setosa
45 5.1 3.8 1.9 0.4 setosa
47 5.1 3.8 1.6 0.2 setosa
99 5.1 2.5 3.0 1.1 versicolor
5 5.0 3.6 1.4 0.2 setosa
8 5.0 3.4 1.5 0.2 setosa
26 5.0 3.0 1.6 0.2 setosa
27 5.0 3.4 1.6 0.4 setosa
36 5.0 3.2 1.2 0.2 setosa
41 5.0 3.5 1.3 0.3 setosa
44 5.0 3.5 1.6 0.6 setosa
50 5.0 3.3 1.4 0.2 setosa
61 5.0 2.0 3.5 1.0 versicolor
94 5.0 2.3 3.3 1.0 versicolor
2 4.9 3.0 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
35 4.9 3.1 1.5 0.2 setosa
38 4.9 3.6 1.4 0.1 setosa
58 4.9 2.4 3.3 1.0 versicolor
107 4.9 2.5 4.5 1.7 virginica
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
25 4.8 3.4 1.9 0.2 setosa
31 4.8 3.1 1.6 0.2 setosa
46 4.8 3.0 1.4 0.3 setosa
3 4.7 3.2 1.3 0.2 setosa
30 4.7 3.2 1.6 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
7 4.6 3.4 1.4 0.3 setosa
23 4.6 3.6 1.0 0.2 setosa
48 4.6 3.2 1.4 0.2 setosa
42 4.5 2.3 1.3 0.3 setosa
9 4.4 2.9 1.4 0.2 setosa
39 4.4 3.0 1.3 0.2 setosa
43 4.4 3.2 1.3 0.2 setosa
14 4.3 3.0 1.1 0.1 setosa
基本統計功能
可以做單一變數統計
x = c(1,2,3,4,5,6,7,8,9,10)
mean(x)
[1] 5.5
min(x)
[1] 1
median(x)
[1] 5.5
基本統計功能 (二)
可以做單一變數統計
max(x)
[1] 10
var(x)
[1] 9.167
summary(x)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 3.25 5.50 5.50 7.75 10.00
使用sapply
透過sapply針對多個欄位操作
sapply(iris[1:4], mean, na.rm=TRUE)
Sepal.Length Sepal.Width Petal.Length Petal.Width
5.843 3.057 3.758 1.199
plyr
可用來分割、套用及合併資料的函式
library(plyr)
ddply(iris, c("Species"), function(df) mean(df$Sepal.Length))
Species V1
1 setosa 5.006
2 versicolor 5.936
3 virginica 6.588
資料繪圖功能 (一)
Pie Chart
table.iris = table(iris$Species)
pie(table.iris)
資料繪圖功能 (二)
Histogram
hist(iris$Sepal.Length)
資料繪圖功能 (三)
Box Plot
boxplot(Petal.Width ~ Species, data = iris)
資料繪圖功能 (四)
Scatter Plot
plot(x=iris$Petal.Length, y=iris$Petal.Width, col=iris$Species)
ggplot2
Scatter Plot
suppressPackageStartupMessages(library(ggplot2))
library(ggplot2)
qplot(Sepal.Length, Petal.Length, data = iris, color = Species)
googleViz
suppressPackageStartupMessages(library(googleVis))
library(googleVis)
GeoMarker <- gvisGeoChart(Andrew, "LatLong", sizevar='Speed_kt',colorvar="Pressure_mb", options=list(region="US"))
plot(GeoMarker)
機器學習與資料分析
資料分析
- 資料處理
- 資料分析
- 解釋分析結果
資料分析與應用
- 統計分析
- 迴歸分析
- 資料分群
- 資料分類
- 推薦系統
- 文字探勘
影像辨識
機器學習
Black-box, algorithmic approach to producing predictions or classifications from data
A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E
– Tom Mitchell (1998)
機器學習主題
- 監督式學習 (Supervised Learning)
- 迴歸分析 (Regression)
- 分類問題 (Classification)
- 非監督式學習 (Unsupervised Learning)
- 降低維度 (Dimension Reduction)
- 分群問題 (Clustering)
迴歸分析
Predict one set of numbers given another set of numbers
Given number of friends x, predict how many goods I will receive on each Facebook posts
Scatter Plot
dataset <- read.csv('fbgood.txt',head=TRUE, sep='\t', row.names=1)
x = dataset$friends
y = dataset$getgoods
plot(x,y)
Linear Fit
plot(x,y)
fit <- lm(y ~ x)
abline(fit, col = 'red', lwd=3)
2nd order polinomial fit
plot(x,y)
polyfit2 <- lm(y ~ poly(x, 2))
lines(sort(x), polyfit2$fit[order(x)], col = 2, lwd = 3)
3rd order polinomial fit
plot(x,y)
polyfit3 <- lm(y ~ poly(x, 3));
lines(sort(x), polyfit3$fit[order(x)], col = 2, lwd = 3)
其他迴歸分析
- MASS rlm - Robust Regression
- GLM - Generalized linear Models
- GAM - Generalized Additive Models
分類問題
Identifying to which of a set of categories a new observation belongs, on the basis of a training set of data
Given features of bank costumer, predict whether the client will subscribe a term deposit
資料敘述
Features:
- age,job,marital,education,default,balance,housing,loan,contact
Labels:
- Customers subscribe a term deposit (Yes/No)
使用LibSVM
library(e1071)
dataset <- read.csv('bank.csv',head=TRUE, sep=';')
set.seed(2)
ind <- sample(2, nrow(dataset), replace = TRUE, prob=c(0.7, 0.3))
train = dataset[ind == 1,]
test = dataset[ind == 2,]
model <- svm(y~., data = train, probability = TRUE)
pred <- predict(model, test[,1:(dim(test)[[2]]-1)], probability = TRUE)
驗證預測能力
table(pred,test[,dim(test)[2]])
pred no yes
no 1196 98
yes 25 46
Confusion Matrix
library(caret)
confusionMatrix(table(pred,test[,dim(test)[2]]))
Confusion Matrix and Statistics
pred no yes
no 1196 98
yes 25 46
Accuracy : 0.91
95% CI : (0.893, 0.925)
No Information Rate : 0.895
P-Value [Acc > NIR] : 0.0333
Kappa : 0.385
Mcnemar's Test P-Value : 8.47e-11
Sensitivity : 0.980
Specificity : 0.319
Pos Pred Value : 0.924
Neg Pred Value : 0.648
Prevalence : 0.895
Detection Rate : 0.876
Detection Prevalence : 0.948
Balanced Accuracy : 0.649
'Positive' Class : no
使用ROC 評估模型
library(ROCR)
pred.prob <- attr(pred, "probabilities")
pred.to.roc <- pred.prob[, 2]
pred.rocr <- prediction(pred.to.roc, as.factor(test[,(dim(test)[[2]])]))
perf.rocr <- performance(pred.rocr, measure = "auc", x.measure = "cutoff")
perf.tpr.rocr <- performance(pred.rocr, "tpr","fpr")
plot(perf.tpr.rocr, colorize=T,main=paste("AUC:",(perf.rocr@y.values)))
論文成就 GET
Support Vector Machine
- e1071 - LIBSVM
- kernlab - SVM, RVM and other kernel learning algorithms
- klaR - SVMlight
- rdetools - Model selection and prediction
降低維度
Seeks linear combinations of the columns of X with maximal variance
Calculate a new index to measure economy index of each Taiwan city/county
台灣經濟指標
- 縣市
- 營利事業銷售額
- 經濟發展支出佔歲出比例
- 得收入者平均每人可支配所得
–2012年《天下雜誌》幸福城市大調查 - 第505期
主成分柱狀圖
dataset <- read.csv('eco_index.csv',head=TRUE, sep=',', row.names=1)
pc.cr <- princomp(dataset, cor = TRUE)
plot(pc.cr)
主成分折線圖
screeplot(pc.cr, type="lines")
abline(h=1, lty=3)
PCA BiPlot
biplot(pc.cr)
PCA BarPlot
barplot(sort(-pc.cr$scores[,1], TRUE))
其他降低維度套件
- kpca - Kernel PCA
- cmdscale - Multi Dimension Scaling
- SVD - Singular Value Decomposition
- fastICA - Independent Component Analysis
分群
物以類聚
Segment customers based on existing features
顧客分群
Clustering by 4 features
- Visit Time
- Average Expense
- Loyalty Days
- Age
決定群數量
mydata <- read.csv('costumer_segment.txt',head=TRUE, sep='\t')
mydata <- scale(mydata)
d <- dist(mydata, method = "euclidean")
fit <- hclust(d, method="ward")
plot(fit)
Cutting Trees
plot(fit)
k1 = 4
groups <- cutree(fit, k=k1)
rect.hclust(fit, k=k1, border="red")
KMEANS 分群
fit <- kmeans(mydata, k1)
plot(mydata, col = fit$cluster)
主成分分析圖
library(cluster)
clusplot(mydata, fit$cluster, color=TRUE, shade=TRUE, lines=0)
其他分群套件
- kernlab - Spectral Clustering
- specc - Spectral Clustering
- fpc - DBSCAN
分析資料一定要Coding 嗎?
Rattle
Big Data
Big Data & R
- RHADOOP
- 延展 R的運算能力
- Hadoop 讓 R 可以進行平行運算
- 不用重新學習新語言(Java 有一定的學習門檻)
- RHIPE
- 並非使用R標準函式
- 以key-value 形式儲存資料，而非Data Frame
- RMPI
- MPI 並行運算的速度及效能較RHadoop 優良
- 可容錯性低，單台伺服器運行錯誤會導致全部工作失敗
RHadoop
RHadoop 架構
Streaming v.s. 原生 Java.
可以讓開發者以其他語言撰寫Mapper/Reducer(R, python, perl)
Mapper, reducer, 及 combiner 程序可以用來導向I/O
Streaming 會因為要開啟腳本語言的虛擬機器，因此會有額外的時間與空間負擔
Thank You
Author: David Chiu
Contact: david@largitdata.com